Hurk, E. (Evelien) van der

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ROTTERDAM SCHOOL OF MANAGEMENT, ERASMUS UNIVERSITYThe business school that thinks and lives in the future Public safety from  a management perspectiveRebecca Morris talks with Gabriele Jacobs and Saskia Bayerl Necessary Condition Analysis: more value from dataBy Jan Dul  Consumer insights: think of yourself when buying for othersBy Gabriele Paolacci Industrial ecosystems:  major opportunities for  port authoritiesBy Frans A.J. Van Den Bosch, Rick M.A. Hollen and Henk W. Volberda  Handling threats to the validity of online dataBy Petra Saskia Bayerl and Babak Akhgar  Enriching the  customer experience with big dataBy Evelien van der Hurk 3rd Quarter 2015Management Knowledge         RSM DISCOVERYwww.rsm.nl/discovery Imagine you must take two trains to get from Schiphol airport to Den Helder. You arrive at the connecting station of Sloterdijk and pull your collar up against the sub-zero temperature wind that is typical of a Dutch winter. A familiar announcement comes over the public address system: due to in-frastructure problems your planned Intercity train has been cancelled. You need a new travel plan.It’s not enough to simply know when the next train departs. You need to know if you will be competing with other people for a seat, or even for standing room. You need to know if you are facing the very real possibility of not being able to get on the train at all. Are there other train routes? Shuttle bus alternatives? Will there even be room for your child’s pram or your folding bike? You are pres-sured to make this decision quickly, because while you are contemplat-ing all these choices, time is ticking away, trains and buses on alternative routes are departing, and your delay is increasing.Although my model focuses specifi-cally on train service disruptions, pro-posing alternative routes and trains to the delayed train passenger, the underlying idea is to offer all buy-ers the highly targeted, customised information that they need to make decisions, improving their own ex-perience and avoiding bottlenecks and backlogs in the purchase and consumption process. The application of this idea extends far beyond public transport. Public transport operators have recently gained access to a wealth of information about their customers with digital records from journey-planner apps and smart travel cards piecing together a comprehensive understanding of individual and general travel-ling habits. New research uses this big data to create personal-ised travel options during train service disruption. How can other industries adopt the same idea to stay competitive in the Age of the Customer?Enriching the customer experience with big dataBy Evelien van der Hurk20   |   3rd Quarter 2015Management KnowledgeRSM DISCOVERYThe age of the customerSupply chains are increasingly shaped by individual buyers. Online reviews of-fer opinions from a buyer’s peers about a product or service; social media is taking over from traditional helplines and customer service desks and has the added effect of bringing custom-er feedback into the public domain to be read or heard by others; apps and other real-time services create an ex-pectation that information will be avail-able to the buyer at every step of the purchase process. In many ways public transport has lagged behind when it comes to using big data to streamline and personalise the customer experience. One of the biggest reasons for this was a simple lack of access to information. Public transport systems traditionally used paper tickets paid for in cash (and of-ten multiple tickets for different modes of public transport) that are intrinsically anonymous. Now that smart travel cards and travel-planning apps are a mainstay of the public transport experience, op-erators have the opportunity to gather data and analyse trends, at last being able to develop strategies to effectively support a travellers’ journeys when dis-ruptions occur. Thanks to the progressive approach of Netherlands Rail (NS) and their en-thusiasm for collaboration and inno-vation, I was able to develop a math-ematical model that does exactly that. However, beyond the scope of my model are a range of possibilities for all kinds of suppliers and providers to “speak” directly to individual customersMeaningful alternativesWhen a supply chain is disrupted we often assume that the customer wants to move as quickly as possible through the process. In this case, when a train is cancelled, we assume that every pas-senger just wants to get home even if it means standing shoulder-to-shoul-der with their fellow passengers on an overcrowded train. But what if this were not the case? What if a great num-ber of people would prefer to remain – not necessarily on the platform but somewhere nearby – delaying the next leg of their journey until the trains were less crowded? Imagine a scenario in which an oper-ator knows that a significant proportion of travellers at a particular time of day are parents with young children. If that operator has developed partnerships with local services and facilities, a can-celled train may be an opportunity to turn an inconvenienced customer into a loyal one. For example, if a nearby in-door playground offers a free coffee to any parent delayed by a train cancella-tion, how many parents would choose to be “bumped” from their intended train in the same way passengers vol-unteer to be bumped from their flight, freeing up space for other passengers? The win-win situation to all parties is clear as operators reduce crowding and bottlenecks, local services increase their own customer base and – most im-portantly – the traveller has full control over their travel decisions and a pleas-ant way to pass the time. Building on the idea of partner-ships (and catering to those passen-gers who cannot wait), a scenario in which a cancelled train triggered an au-tomatic alert to ride-sharing networks like BlaBlaCar or to local taxi services would offer passengers an extra travel alternative, one that can be used al-most immediately while still avoiding bottlenecks on other trains. Similarly, although train service op-erators contract with local bus compa-nies to provide shuttle alternatives to cancelled trains, the amount of time needed to arrange extra buses and drivers is prohibitive. Effective use of big data in this scenario could provide a passenger with a clear journey plan using already-scheduled buses, reduc-ing or even eliminating wait times for specially arranged services. 3rd Quarter 2015   |   21“…beyond the scope of my model are a range of possibilities for all kinds of suppliers and providers to ‘speak’ directly to individual customers.”www.rsm.nl/discovery company’s servers. I could also store this information myself locally on my phone and use an app to help me find my favourite products on sale – or maybe even propose alternatives if my preferred product is unavailable. Third parties could even develop this app. The convenient shopping experi-ence, and possible discounts, may still attract me to shop at Albert Heijn – cre-ating a win-win situation without hav-ing to share all my information.Using information to streamline and personalise the customer experi-ence will become more important as mobility and interconnectedness in-creases. When large services like NS are heavily investing in streamlining their customers’ experiences it can only become more interesting to see where they go next. This article is based on Evelien van der Hurk's PhD thesis Passengers, Information and Disruption, which can be downloaded at  WEB  http://repub.eur.nl/pub/78275Evelien van der Hurk obtained her PhD from RSM in June 2015, and currently works as an assistant professor at the Technical University of Denmark.  EMAIL  evdh@transport.dtu.dkone arriving before any train on any route arrives according to the timeta-ble, as the check-in and out times are not synchronized with the registered train-driving times.Synchronisation of the clocks in all data sets would significantly increase the value of the data itself – but once the system is in place, correcting this is usually far from easy. Therefore it is important that companies focus not only on storing data but also on stor-ing it in such a way that it is consistent with other (existing) systems – and also consistent over time.Consumer concerns about privacy are also significant. While there is no re-quirement to track individuals in order to improve service (anonymous data fol-lows patterns of behaviour, not names and addresses), big data can sometimes be akin to Big Brother in a customer’s mind. To allay these concerns, it be-comes even more important for com-panies to use data to support a custom-er, and not only for their own benefit. Right now, a company collects data and we hope that they use it wisely. In the future a customer can easily be the custodian of their own data while benefiting from the applications it of-fers. For example, when shopping at supermarket Albert Heijn there is no need for my grocery list to be on the “In the future a customer can easily be the custodian of their own data while benefiting from the applications it offers.”Obstacles to using big dataWith so much information availa-ble, effective harvesting is crucial. Although the focus of many compa-nies is on storing lots of data, the po-tential applications of big data are se-verely diminished if data is gathered and stored inconsistently. These in-consistencies often only show up dur-ing analysis. For example, in the analy-sis of data from NS we found that the asynchronicity of clocks in different data sets caused many problems and we had to discard a significant amount of data. Imagine, for example, some-Management KnowledgeRSM DISCOVERYEnriching the customer experience with big data (continued)  By Evelien van der Hurk22   |   3rd Quarter 2015  RSM Discovery  videosWatch a video of Evelien van den Hurk explaining the practical insights and applications offered by her research at WEB   http://rsm.nl/rsmdiscovery23/ 

Enriching the customer experience with big data

http://repub.eur.nl/pub/78690/RSMDiscovery-EvdHurk.pdf

Enriching the customer experience with big data

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